Optimization of Ultra-High and High Manganese Steel Based on Artificial Neural Network and Genetic Algorithm

Five algorithms of Gaussian process regression, artificial neural network (ANN), support vector machine, boosted trees, and genetic algorithm artificial neural networks (GAANN) are used to model high manganese steel's processing parameters, chemical composition, and mechanical properties. The results show that the ANN model optimized by applying the GAANN with topology [25, 25] has the highest prediction accuracy. Based on the network calculated using the GAANN, the price optimization of the target performance is achieved by introducing the price factor and the target performance in the fitness function. The NSGA-II algorithm is applied to design ultra-high manganese steel's processes and chemical composition. The predicted performance is much higher than the highest value in the original data, and the calculation results all have an accuracy of about 94%. The developed material design model is applicable to high manganese steel and can be used to design other alloys, which provides a good direction for machine learning to design multi-component alloy materials.